Tristan Hearn thearn
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| import pprocess | |
| import time | |
| import numpy as np | |
| # Define a function to parallelize: | |
| def takeuptime(x): | |
| """A function to waste CPU cycles""" | |
| time.sleep(0.1) | |
| return x[-1] | |
thearn
/ gzip_pickle.py
Created
April 20, 2013 00:57
Functions for saving and loading python objects using pickle with gzip compression.
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| import pickle | |
| import gzip | |
| def save(object, filename, protocol = 0): | |
| """Saves a compressed object to disk | |
| """ | |
| file = gzip.GzipFile(filename, 'wb') | |
| file.write(pickle.dumps(object, protocol)) | |
| file.close() |
thearn
/ svd_approximate.py
Last active
January 8, 2024 20:25
Function to generate an SVD low-rank approximation of a matrix, using numpy.linalg.svd. Can be used as a form of compression, or to reduce the condition number of a matrix.
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| import numpy as np | |
| def low_rank_approx(SVD=None, A=None, r=1): | |
| """ | |
| Computes an r-rank approximation of a matrix | |
| given the component u, s, and v of it's SVD | |
| Requires: numpy | |
| """ |
thearn
/ fft_convolution.py
Last active
November 18, 2023 09:47
1D and 2D FFT-based convolution functions in Python, using numpy.fft
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| from numpy.fft import fft, ifft, fft2, ifft2, fftshift | |
| import numpy as np | |
| def fft_convolve2d(x,y): | |
| """ 2D convolution, using FFT""" | |
| fr = fft2(x) | |
| fr2 = fft2(np.flipud(np.fliplr(y))) | |
| m,n = fr.shape | |
| cc = np.real(ifft2(fr*fr2)) | |
| cc = np.roll(cc, -m/2+1,axis=0) |
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